The ability to segment structures that protrude or depress from a surface is desirable in computer vision and medical applications. Such an ability is attained relatively easily for man-made objects when the structures to be segmented have strong geometrical definition. This is because such structures with strong geometrical definition can be easily approximated using geometric primitives. Unfortunately, in more natural settings such as anatomical surfaces, for example, approximations using geometric primitives may not be applicable, and image noise may become a large factor in the segmentation process.
Computer aided design and computer vision literature has many instances of approaches aimed at recovering parametric descriptions of surfaces based on the analysis of the residual of fitting multiple parametric surfaces or volumes. Other approaches may use curvature to identify the loci where the Mean or the Gaussian curvature indicate a peak.
Accordingly, it is desirable to provide a segmentation approach applicable to natural settings such as medical and non-medical images in the presence of surface noise.